Machine Learning Research Engineer
: Job Details :

Representation Learning | Long Context Sequence Modeling | Tabular Data | Statistics | Software Engineering | Biological Systems | GenomicsFully remote | Top quartile salary | Stock options | 401K | Health, dental and vision insuranceWe are hiring an ML research engineer to interface large-scale representation learning methods with experimental genomic and transcriptomic datasets. Fully remote.Join a team of world-leading experts in this domain and help drive the field forward as we tackle a problem space only recently made tractable by advances in ML and HPC.The roleUse your expertise to develop foundational models based on biological and biochemical data corpuses for applications in early stage drug discoveryProductionalize research code and add features to state of the art modelsImplement and train baseline models for downstream tasksIdentify, implement, and maintain model performance metrics and indicatorsLead and/or contribute to publications in top-tier journalsStay up to date with relevant research and industry trends for integration into our productsTake on a grand challenge and share our purposeful mission - we're building a biological systems simulator that will transform how medicines are developed - you will be making it accessible and powerful for scientists and doctors, with a big downstream impact on the wellbeing of millions of patientsCompetitive compensation - A starting salary in the top quartile for role and level, based on local benchmarksStock options - you are joining an early-stage startup we want you to have Ë skin in the game' and your options package will reflect thisSelf-manage - we are a distributed-first company, working from home and collaborating asynchronously from Seattle, NY, Vermont, and ManchesterAbout youMasters in Computer Science, Engineering, or other STEM field and 4+ years of experience in applied machine learning and software engineeringPhD in Computer Science, Engineering, or other STEM field and 2+ years of experience in applied machine learning and software engineeringKnowledge of statistics for scientific data (e.g. Design of Experiments)Experience working within the domain of biological systemsExperienced with large scale representation learningExperienced with long context sequence modelsExperience with tabular dataExperience with ML + data science libraries such as PyTorch, Pandas, Scikit-learn etc.Python ExpertiseFamiliarity with cloud platforms like AWSUnderstanding of version control (Git) and software engineering best practicesExperience with early-stage startupsAbout usOur missionWe are on a mission to provide access to more effective medicines for millions of patients. We're building a model of human molecular physiology for research scientists and clinicians that can answer the fundamental question, will it work? Every modernized field of engineering has a systems simulator to test complex interactions in bits rather than atoms. This doesn't yet exist for biology. Without one, drug development is expensive because risk of failure is very high; 30-60% of prescribed medicines have no clinical benefit to patients and adverse reaction to treatment is the 4th leading cause of death in the US, ahead of pulmonary disease and diabetes. Syntensor is taking on this grand challenge, developing fundamental machine learning methods and applying them at scale to biological data so every individual patient can be prescribed the most effective, least toxic treatment possible.What we doWe are productionizing and scaling up a generalizable machine learning platform that predicts efficacy and toxicity for any drug in any indication. We are using an extensive, heterogeneous biomedical graph, novel fundamental ML methods and advanced engineering infrastructure to generate and explain model outputs for users of our app. Currently, our users are research scientists involved in drug development.The teamWe are a small team of people with diverse skills and a shared bias towards problem solving and execution. We are inventors and builders who believe in the scientific method; feedback and iteration is essential to our process and we share our work early and often. That said, we aim high. Our mission and the domain in which we operate demand that we take on some of the hardest problems researchers, scientists, engineers and designers face, and we are determined to build technology that solves them properly and usefully for users of our platform. We are looking for talented people who are motivated by the challenge of hard problems and who are already curious about the technological, scientific or cultural domains with which we engage.We are a distributed-first team and very relaxed about where and when work happens, but come together as a whole team weekly to sync-up. We work with intrinsic curiosity and motivation towards well defined goals (even where there are unknown unknowns). Our diversity, great communication and respectful, supportive teamwork make us highly effective.#J-18808-Ljbffr